2022
DOI: 10.3390/pr10020312
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The Prediction of Spark-Ignition Engine Performance and Emissions Based on the SVR Algorithm

Abstract: Engine development needs to reduce costs and time. As the current main development methods, 1D simulation has the limitations of low accuracy, and 3D simulation is a long, time-consuming task. Therefore, this study aims to verify the applicability of the machine learning (ML) method in the prediction of engine efficiency and emission performance. The support vector regression (SVR) algorithm was chosen for this paper. By the selection of kernel functions and hyperparameters sets, the relationship between the o… Show more

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Cited by 21 publications
(6 citation statements)
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“…SVR has already received a lot of interest because of its ability to represent complicated structures. The basic principle of the SVR model is concisely given in this paper 31 , 32 .…”
Section: The Theory Of ML Modelsmentioning
confidence: 99%
“…SVR has already received a lot of interest because of its ability to represent complicated structures. The basic principle of the SVR model is concisely given in this paper 31 , 32 .…”
Section: The Theory Of ML Modelsmentioning
confidence: 99%
“…The predictions were based on analysing scalar field contours within the cylinder at the exhaust valve opening phase. The scalar field examining this work includes equivalence ratio, temperature, velocity and turbulent energy Zhang et al ( 2022c ) Multistate deep reinforcement learning (M-DRL) This study presented a novel energy management strategy based on M-DRL with a hybrid action space combining discrete and continuous elements. The state space is expanded to integrate real-time multivariate traffic and terrain information, enhancing the accuracy of the energy management system Li et al ( 2007 ) Gaussian process regression (GPR) GPR model demonstrated exceptional accuracy in predicting performance, temperature and emission values under both steady-state and transient operating conditions, when compared to other regression modeling methods Wong et al ( 2015 ) Sparse Bayesian extreme learning machine (SBELM) In relation to the duration of execution, the size of the model, and its ability to withstand fluctuations in the quantity of hidden neurons, SBELM demonstrates superiority over other approaches.…”
Section: Introductionmentioning
confidence: 99%
“…Numerous researchers have employed machine learning methodologies for the anticipation of engine-related parameters. According to Zhang et al [24], the utilization of support vector regression algorithm in numerical simulations has proven to be highly effective in forecasting engine performance and emissions. Karunamurthy et al [25] conducted a comprehensive review of various machine learning methodologies and algorithms, including artificial neural networks, random forest, semi supervised fuzzy and support vector machine, as utilized by multiple researchers.…”
Section: Introductionmentioning
confidence: 99%